Emotional intelligence and hot and cool cognitive control ability

Emotional intelligence (EI) is the ability to perceive, use, understand, and regulate emotions. The EI construct has been organized following three main approaches: performance-based ability model, a self-report mixed model, and a self-report ability model. EI appears to be beneficial to the performance of “hot” (i.e., emotionally laden) cognitive tasks when using performance-based ability models but not when using self-report EI models. The aim of the present study is to analyze the relationship between the three models of EI and cognitive control ability during the performance of hot and “cool” (i.e., non-emotionally laden) “go/no-go” tasks. 187 undergraduate students participated in the experimental design. They completed the three EI tests of interest as well as go/no-go tasks; the stimuli used for the hot and cool cognitive task were faces and geometric figures, respectively. Results show that individuals with higher EI, measured through the performance-based ability test, perform better on a hot cognitive control task. Specifically, we provide evidence for negative associations between the “managing” branch of EI measured through the performance-based ability test of EI and the cognitive control index of the hot go/no-go task; when using the self-report EI instruments, no consistent findings were achieved. The study found no such results with the cool task. Findings in terms of the validity and different implications of the different EI models are discussed.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Towards a Model of Testers\u27 Cognitive Processes: Software Testing as a Problem Solving Approach

Software testing is a complex, intellectual activity based (at least) on analysis, reasoning, decision making, abstraction and collaboration performed in a highly demanding environment. Naturally, it uses and allocates multiple cognitive resources in software testers. However, while a cognitive psychology perspective is increasingly used in the general software engineering literature, it has yet to find its place in software testing. To the best of our knowledge, no theory of software testers\u27 cognitive processes exists. Here, we take the first step towards such a theory by presenting a cognitive model of software testing based on how problem solving is conceptualized in cognitive psychology. Our approach is to instantiate a general problem solving process for the specific problem of creating test cases. We then propose an experiment for testing our cognitive test design model. The experiment makes use of verbal protocol analysis to understand the mechanisms by which human testers choose, design, implement and evaluate test cases. An initial evaluation was then performed with five software engineering master students as subjects. The results support a problem solving-based model of test design for capturing testers\u27 cognitive processes

Towards a Model of Testers' Cognitive Processes: Software Testing as a Problem Solving Approach

Software testing is a complex, intellectual activity based (at least) on analysis, reasoning, decision making, abstraction and collaboration performed in a highly demanding environment. Naturally, it uses and allocates multiple cognitive resources in software testers. However, while a cognitive psychology perspective is increasingly used in the general software engineering literature, it has yet to find its place in software testing. To the best of our knowledge, no theory of software testers' cognitive processes exists. Here, we take the first step towards such a theory by presenting a cognitive model of software testing based on how problem solving is conceptualized in cognitive psychology. Our approach is to instantiate a general problem solving process for the specific problem of creating test cases. We then propose an experiment for testing our cognitive test design model. The experiment makes use of verbal protocol analysis to understand the mechanisms by which human testers choose, design, implement and evaluate test cases. An initial evaluation was then performed with five software engineering master students as subjects. The results support a problem solving-based model of test design for capturing testers' cognitive processes.Comment: (v3) minor issues fixed, Accepted and presented in the IEEE International Workshop on Human and Social Aspects of Software Quality (HASQ 2020

Alan Turing and the “hard” and “easy” problem of cognition: doing and feeling

The "easy" problem of cognitive science is explaining how and why we can do what we can do. The "hard" problem is explaining how and why we feel. Turing's methodology for cognitive science (the Turing Test) is based on doing: Design a model that can do anything a human can do, indistinguishably from a human, to a human, and you have explained cognition. Searle has shown that the successful model cannot be solely computational. Sensory-motor robotic capacities are necessary to ground some, at least, of the model's words, in what the robot can do with the things in the world that the words are about. But even grounding is not enough to guarantee that -- nor to explain how and why -- the model feels (if it does). That problem is much harder to solve (and perhaps insoluble)

Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for healthcare policy and decision making

Objective: Network meta-analyses have extensively been used to compare the effectiveness of multiple interventions for healthcare policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds, in one simultaneous analysis. Study design and setting: Motivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE <25/30 and <27/30, and MoCA <22/30 and <26/30. Using Markov Chain Monte Carlo (MCMC) methods, we fitted a bivariate network meta-analysis model incorporating constraints on increasing test threshold, and accounting for the correlations between multiple test accuracy measures from the same study. Results: We developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold <26/30 appeared to have the best true positive rate, whilst MMSE at threshold <25/30 appeared to have the best true negative rate. Conclusion: The combined analysis of multiple tests at multiple thresholds allowed for more rigorous comparisons between competing diagnostics tests for decision making

Reproducibility of cognitive endpoints in clinical trials: Lessons from neurofibromatosis type 1

OBJECTIVE: Rapid developments in understanding the molecular mechanisms underlying cognitive deficits in neurodevelopmental disorders have increased expectations for targeted, mechanism-based treatments. However, translation from preclinical models to human clinical trials has proven challenging. Poor reproducibility of cognitive endpoints may provide one explanation for this finding. We examined the suitability of cognitive outcomes for clinical trials in children with neurofibromatosis type 1 (NF1) by examining test-retest reliability of the measures and the application of data reduction techniques to improve reproducibility. METHODS: Data were analyzed from the STARS clinical trial (n = 146), a multi-center double-blind placebo-controlled phase II trial of lovastatin, conducted by the NF Clinical Trials Consortium. Intra-class correlation coefficients were generated between pre- and post-performances (16-week interval) on neuropsychological endpoints in the placebo group to determine test-retest reliabilities. Confirmatory factor analysis was used to reduce data into cognitive domains and account for measurement error. RESULTS: Test-retest reliabilities were highly variable, with most endpoints demonstrating unacceptably low reproducibility. Data reduction confirmed four distinct neuropsychological domains: executive functioning/attention, visuospatial ability, memory, and behavior. Test-retest reliabilities of latent factors improved to acceptable levels for clinical trials. Applicability and utility of our model was demonstrated by homogeneous effect sizes in the reanalyzed efficacy data. INTERPRETATION: These data demonstrate that single observed endpoints are not appropriate to determine efficacy, partly accounting for the poor test-retest reliability of cognitive outcomes in clinical trials in neurodevelopmental disorders. Recommendations to improve reproducibility are outlined to guide future trial design

Advanced Screencasting With Embedded Assessments in Pathophysiology and Therapeutics Course Modules

Objective. To implement and assess the effectiveness of a hybrid learning model using advanced screencasting with embedded assessments in pathophysiology and therapeutics modules. Design. Two pathophysiology and therapeutics course modules on viral hepatitis and the clinical pharmacokinetics of aminoglycosides were chosen for study. The preclass portion of the hybrid model involved student completion of interactive e-lectures that were created with the use of advanced screencasting and included embedded assessments. Students viewed the e-lectures and completed the assessment questions prior to in-class lecture. Assessment. Preimplementation and postimplementation test scores were compared and student survey data were analyzed. Test scores improved significantly and students’ perceptions of the learning method were favorable. Test scores improved most significantly on higher-level Bloom’s taxonomy questions. Conclusion. A hybrid model that used advanced screencasting with embedded assessments offered a novel method to afford students active-learning opportunities to progress to higher cognitive domains of learning

Pengaruh Model Problem Based Learning (PBL) Terhadap Kemampuan Memecahkan Masalah Dan Hasil Belajar Kognitif Siswa Biologi SMA

: This research aimed to determine the effect of Problem Based Learning (PBL) model on students' problem solving abilities and cognitive learning outcomes elaborating an experimental research design. The research data was collected through a pre-test and post-test using a rubric to determine the students' problem solving abilities and cognitive learning outcomes. The results showed that PBL model significantly improved problem solving abilities and cognitive learning outcomes of students by 17.73% and 23.65%, respectively. . Based on these results, the researchers suggested that teachers use the model of Problem Based Learning (PBL) consistently because it has proven its success on the ability to solve problems and cognitive achievement of students

COGNITIVE EMPOWERMENT MODEL DEVELOPMENT IN IMPROVING THE PERFORMANCE OF NURSES AT RSUD IBNU SINA GRESIK

Introduction: Cognitive empowerment model of Thomas and Velthouse shows the importance of encouraging nurses to motivate yourself. The main objective of this study was to develop a model of cognitive empowerment in improving the performance of nurses in hospitals Ibnu Sina. Methods: This study used two stages, the first stage of the survey research design using a descriptive explanation then tested the model using Partial Least Square. The second stage was the empowerment of the cognitive test module, the research design was quasi-experimental (quasy experiment) were then tested Wilcoxon signed rank test and Mann Whitney. Result: Test Partial Least Square showed that psychological factors with path coefficients 0399 and the T-statistic 2,072 affect the empowerment of cognitive, individual factors with the path coefficient 0.335 and T-statistic 2,249, factors interpretarive syle with path coefficients 0,307 and the T-statistic 2,463 affect the empowerment of cognitive , while the leadership does not affect the enviroment event 0.035dan path coefficients T-statistics 0.505. Cognitive empowerment with path coefficients 0697 and the T-statistic 6.093 affect the performance. Wilcoxon statistical test showed there was effect on the performance of cognitive empowerment.Discuss and conclution: Psychological factors, individual characteristics and interpretative style is a factor that affects cognitive empowerment, which in turn can improve the performance of nurses. Further research can continue this research by applying the empowerment module elsewhere, so the module has been made more aplicable (can be used). Keywords: model development, empowerment of cognitive performanc

The Implementation of Cognitive Conflict Learning Strategy in Efforts to Reduce Heat Misconception in Junior High School Students

The most misconceptions in learning physics originate from the students themselves which include the students' initial concepts, associative and humanistic thinking, wrong intuition, the stages of students' cognitive development, and students' abilities. The purpose of this study was to determine the profile of heat misconception in junior high school students after learning by using the TGT cooperative learning model with cognitive conflict strategies. The research design used in this study is One group pre-test and post-test design. The data collection instrument was a misconception test in the form of a Three-tier Test totaling 20 questions. The results showed that before the treatment of the five junior high schools had an average percentage of misconceptions of 54.37%. After learning using the TGT cooperative learning model with cognitive conflict strategies the average percentage of students' misconceptions is 37.58%. Thus, there was a decrease in the percentage of misconceptions by 16.79%. The conclusion of this study is the TGT cooperative learning model with cognitive conflict strategies can reduce heat misconception in junior high school students in Lamongan District